Printed natural-language documents continue to represent a widely used communications medium among individuals, within organizations, and for distribution of information among information consumers. With the advent of ubiquitous and powerful computational resources, including personal computational resources embodied in smart phones, pads, tablets, laptops, and personal computers, as well as larger-scale computational resources embodied in cloud-computing facilities, data centers, and higher-end servers within various types of organizations and commercial entities, natural-language information is, with increasing frequency, encoded and exchanged in electronic documents. Printed documents are essentially images, while electronic documents contain sequences of numerical encodings of natural-language symbols and characters. Because electronic documents provide advantages in cost, transmission and distribution efficiencies, ease of editing and modification, and robust-storage over printed documents, an entire industry supporting methods and systems for transforming printed documents into electronic documents has developed over the past 50 years. Computational optical-character-recognition methods and systems and electronic scanners together provide reliable and cost-effective imaging of printed documents and computational processing of the resulting digital images of text-containing documents to generate electronic documents corresponding to the printed documents. Designers, developers, manufacturers, and venders of computational optical-character-recognition methods and systems constantly seek new methods, systems, and subsystems that efficiently implement the many different types of automated processes applied to digital images during preparation of digital images for automated optical character recognition,